摘要菌群优化算法（BFO||BFOA）是近年来的一种新型的智能优化算法，此算法具有容易实现、收敛性快、易优化等特点，在模式识别、优化函数、工业控制等许多领域等有着广泛应用。该智能优化算法在解决寻优问题的领域内有着广泛的应用，其在觅食时所表现的的聚集行为的智能性对菌群优化算法来而言，不仅是其重要的理论基础，而且还对该算法的进一步研究应用有着十分重要的意义。84429

本文针对改变算法中因为步长的不可变性而导致的易错过局部最优解，寻优能力差等缺点，通过随着周遭环境的变化而变化的趋化行动中的步长，来体现细菌个体通过信息交流与合作在整体层面表现出更高的智能的特性，显著提升此算法的有效性和应用性。通过数个函数所进行的算法性能测试所得出的结果与其他几个典型的智能仿生算法进行寻找最优解的比较，能够得出经过改进的算法在寻优能力、搜索能力、收敛速度和稳定性等方面上比其他算法有着显著的提升，此结果验证了本文的优化算法的有效性。

本次仿真进行的是关于细菌觅食算法的优化，目前只有理论上的研究，关于如何在工业上的应用及其能否起到的实际效果并没有十足的把握。

毕业论文关键词：菌群优化算法；趋化步长的优化；智能性

Abstract  Bacterial foraging optimization algorithm (BFO||BFOA) is a new intelligent optimization algorithm, this algorithm has the characteristics of easy implementation, fast convergence, easy optimization, and so on。 It is widely used in pattern recognition, function optimization, control and so on。

  Intelligent aggregation behavior is not only an important theoretical basis, but also a very important significance for the further research and application of the algorithm。

  In comparison of results obtained from tests and lead to variability in view of changing the algorithm for step is easy to miss the local optimal solution and optimization ability is poor shortcomings, with the surrounding environment changes and changes of the chemotactic action step, to reflect inpidual bacteria through information exchange and cooperation in the overall level, the higher the intelligent characteristics, significantly improve the algorithm's validity and application。 Through a number of functions of the performance of the algorithm and other several typical intelligent bionic algorithm to find the optimal solution can be obtained by the improved algorithm optimization, search ability, convergence speed and stability than other algorithms This result verifies the effectiveness of the optimization algorithm in this paper。

  The simulation is carried out on the optimization of bacterial foraging algorithm, there is only theoretical research on how the industrial application and its ability to play the actual effect is not fully grasp。

Keywords: bacterial  foraging  optimization、Optimization of chemotaxis step size、  intelligence

目录

第一章 绪论 1

1。1 研究背景和意义 1

1。2 智能仿生算法 1

1。2。1  遗传算法 2

1。2。2  蚁群算法 4

1。2。3  人工鱼群算法 5

1。2。4  粒子群算法 7

1。2。5  菌群优化算法 8

1。3  本文的主要内容和安排 9

1。3。1  本文的主要内容